Impacts of upwind wildfire emissions on CO, CO_2, and PM_2.5 concentrations in Salt Lake City, Utah

Abstract

Biomass burning is known to contribute large quantities of CO 2 , CO, and PM 2.5 to the atmosphere.Biomass burning not only affects the area in the vicinity of fire but may also impact the air quality far downwind from the fire. The 2007 and 2012 western U.S. wildfire seasons were characterized by significant wildfire activity across much of the Intermountain West and California. In this study, we determined the locations of wildfire-derived emissions and their aggregate impacts on Salt Lake City, a major urban center downwind of the fires. To determine the influences of biomass burning emissions, we initiated an ensemble of stochastic back trajectories at the Salt Lake City receptor within the Stochastic Time-Inverted Lagrangian Transport (STILT) model, driven by wind fields from the Weather Research and Forecasting (WRF) model. The trajectories were combined with a new, high-resolution biomass burning emissions inventory-the Wildfire Emissions Inventory. Initial results showed that the WRF-STILT model was able to replicate many periods of enhanced wildfire activity observed in the measurements. Most of the contributions for the 2007 and 2012 wildfire seasons originated from fires located in Utah and central Idaho. The model results suggested that during intense episodes of upwind wildfires in 2007 and 2012, fires contributed as much as 250 ppb of CO during a 3 h period and 15 μg/m 3 of PM 2.5 averaged over 24 h at Salt Lake City. Wildfires had a much smaller impact on CO 2 concentrations in Salt Lake City, with contributions rarely exceeding 2 ppm enhancements.

Confirmation of Elevated Methane Emissions in Utah's Uintah Basin With Ground‐Based Observations and a High‐Resolution Transport Model

“…The STILT‐produced backward trajectories are used to calculate footprints—the sensitivity of concentrations at the receptor due to upwind surface influences at each 0.01° grid box (ppm (μmol m −2 s −1 ) −1 ). More specifically, surface flux footprints f ( x r , t r | x i , y j , t m ) for a given receptor located at x r at time t r from an upwind source at ( x i , y j ) and past time t m are a function of the number of Lagrangian particles residing in the planetary boundary layer (PBL) for that upwind location as given by the equation below: where m air is the molecular weight of air, h is the height of the volume in which the surface fluxes are diluted over, ρ is the average density of all particles, N tot is the total number of particles, and Δ t p , i , j , k is the amount of time a particle p spends within the volume at location ( x i , y j ) and time t m (Lin et al, ; Mallia et al, ).…”

“…where m air is the molecular weight of air, h is the height of the volume in which the surface fluxes are diluted over, ρ is the average density of all particles, N tot is the total number of particles, and Δt p,i,j,k is the amount of time a particle p spends within the volume at location (x i , y j ) and time t m (Lin et al, 2003;Mallia et al, 2015).…”

Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India

“…We perform 2012-2016 simulations of daily surface PM 2.5 in Delhi using the Stochastic Time-Inverted Lagragian Transport (STILT) model (Lin et al 2003), driven by 0.5 • × 0.5 • Global Data Assimilation meteorology (GDAS; https://ready.arl.noaa.gov/gdas1.php). STILT is a receptor-oriented Lagrangian particle dispersion model (appendix S2), and has been used previously to assess the influence of wildfires on urban air pollution (Mallia et al 2015). Figure 1 shows the spatial footprint of the median 2012-2016 sensitivities of a Delhi receptor (28.62 • N, 77.21 • E) to the surrounding emissions during the burning season.…”

How can mountaintop CO<sub>2</sub> observations be used to constrain regional carbon fluxes?

“…1; Schimel et al, 2002;Monson et al, 2002;Wharton et al, 2012), albeit this storage is highly sensitive to environmental drivers such as temperature and water availability (Monson et al, 2006;Schwalm et al, 2012;Wharton et al, 2012;Potter et al, 2013) as well as disturbances such as insect infestation (Negron and Popp, 2004) and wildfires (Wiedinmyer and Neff, 2007). These disturbances are also coinciding with rapid population increases in this region (Lang et al, 2008), with concomitant rise in urban CO 2 emissions (Mitchell et al, 2017), urban-wildland interfaces (Mell et al, 2010), and demands for water resources (Reisner, 1993;Gollehon and Quinby, 2000).…”